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The course deals with the basic principles needed to understand and apply Machine Learning models and methods. The topics include Supervised and Unsupervised Learning, Bayesian Decision Theory, Parametric Methods, Tuning Model Complexity, Dimensionality Reduction, Clustering, Nonparametric Methods, Decision Trees, Comparing and Combining Algorithms, as well as a few other applications of these methods.
There are no prerequisites for this course.
4 Days/Lecture & Lab
This course is designed for those who want to learn the basic principles needed to understand and apply Machine Learning models and methods.
- Operators and Functions in R
- Fundamentals of R graphics and Subscripting
- Writing Functions in R
- Reading data files into R and Printing using formatting
- Vectored programming and mapping Functions
- Statistical Modelling with R
- Big Data Analytics